{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2023-11-27T13:25:26.451097600Z",
     "start_time": "2023-11-27T13:25:26.405745600Z"
    }
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "\n",
    "from gensim.corpora import Dictionary\n",
    "import logging\n",
    "from warnings import filterwarnings\n",
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import spacy\n",
    "import gensim\n",
    "from gensim.models import LdaModel\n",
    "from gensim.models import CoherenceModel\n",
    "import pyLDAvis.gensim_models\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "filterwarnings('ignore', category=DeprecationWarning)\n",
    "logging.basicConfig(level=logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "488cbabbfb48268e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T13:25:26.462107100Z",
     "start_time": "2023-11-27T13:25:26.431480Z"
    }
   },
   "outputs": [],
   "source": [
    "class EnLDA:\n",
    "    def __init__(self):\n",
    "        self.nlp = spacy.load(\"en_core_web_lg\")\n",
    "\n",
    "    def participle(self,text):\n",
    "        doc = self.nlp(text)\n",
    "        return [token.lemma_.lower() for token in doc if token.text not in stop_words and token.lemma_.lower() not in my_stop_word and not token.is_stop and not token.is_punct and len(token.text.strip())>3]\n",
    "    \n",
    "    # 构建LDA模型\n",
    "    @staticmethod\n",
    "    def build_lda_model(corpus, dictionary, num_topics):\n",
    "        lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=num_topics)\n",
    "        return lda_model\n",
    "    \n",
    "    \n",
    "    # 寻找最佳主题数量\n",
    "    def find_best_topic_number(self, corpus, dictionary, texts, max_topics=10):\n",
    "        coherence_scores = []\n",
    "        perplexity_scores = []\n",
    "    \n",
    "        for num_topics in tqdm(range(1, max_topics + 1)):\n",
    "            lda_model = self.build_lda_model(corpus, dictionary, num_topics)\n",
    "            coherence_model = CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "            coherence_score = coherence_model.get_coherence()\n",
    "            coherence_scores.append(coherence_score)\n",
    "    \n",
    "            perplexity_score = lda_model.log_perplexity(corpus)\n",
    "            perplexity_scores.append(perplexity_score)\n",
    "    \n",
    "        # 画图展示\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.plot(range(1, max_topics + 1), coherence_scores, marker='o')\n",
    "        plt.title('Coherence Score vs. Number of Topics')\n",
    "        plt.xlabel('Number of Topics')\n",
    "        plt.ylabel('Coherence Score')\n",
    "    \n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.plot(range(1, max_topics + 1), perplexity_scores, marker='o')\n",
    "        plt.title('Perplexity Score vs. Number of Topics')\n",
    "        plt.xlabel('Number of Topics')\n",
    "        plt.ylabel('Perplexity Score')\n",
    "    \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "        # 寻找最佳主题数量\n",
    "        best_num_topics = range(1, max_topics + 1)[coherence_scores.index(max(coherence_scores))]\n",
    "        return best_num_topics\n",
    "\n",
    "    \n",
    "    def run(self, document):\n",
    "        processed_texts = [self.participle(text) for text in document]\n",
    "        # 构建LDA模型所需的语料库和字典\n",
    "        dictionary = gensim.corpora.Dictionary(processed_texts)\n",
    "        corpus = [dictionary.doc2bow(text) for text in processed_texts]\n",
    "        best_num_topics = self.find_best_topic_number(corpus, dictionary, processed_texts, max_topics=10)\n",
    "\n",
    "        # 构建最终的LDA模型\n",
    "        final_lda_model = self.build_lda_model(corpus, dictionary, best_num_topics)\n",
    "        res = final_lda_model.print_topics(num_topics=best_num_topics, num_words=50)\n",
    "        for topic,words in res:\n",
    "            print(f\"主题{topic+1}\",\", \".join(re.findall('\"(.*?)\"',words)))\n",
    "        \n",
    "        # 可视化主题\n",
    "        pyLDAvis.enable_notebook()\n",
    "        vis = pyLDAvis.gensim_models.prepare(final_lda_model, corpus, dictionary)\n",
    "        pyLDAvis.save_html(vis,save_path+\"英文主题建模结果.html\")\n",
    "        print(\"英文主题建模结果已保存至\",save_path+\"英文主题建模结果.html\")\n",
    "\n",
    "    def run2(self,document,best_num_topics):\n",
    "        processed_texts = [self.participle(text) for text in document]\n",
    "        # 构建LDA模型所需的语料库和字典\n",
    "        dictionary = gensim.corpora.Dictionary(processed_texts)\n",
    "        corpus = [dictionary.doc2bow(text) for text in processed_texts]\n",
    "        # 构建最终的LDA模型\n",
    "        final_lda_model = self.build_lda_model(corpus, dictionary, best_num_topics)\n",
    "        res = final_lda_model.print_topics(num_topics=best_num_topics, num_words=50)\n",
    "        for topic,words in res:\n",
    "            print(f\"主题{topic+1}\",\", \".join(re.findall('\"(.*?)\"',words)))\n",
    "        \n",
    "        # 可视化主题\n",
    "        pyLDAvis.enable_notebook()\n",
    "        vis = pyLDAvis.gensim_models.prepare(final_lda_model, corpus, dictionary)\n",
    "        pyLDAvis.save_html(vis,save_path+\"英文主题建模结果.html\")\n",
    "        print(\"英文主题建模结果已保存至\",save_path+\"英文主题建模结果.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "my_stop_word = [\"recommended\",\"skills\",\"position\",\"equal\"]+['role', 'work address', 'education requirement', 'work time', 'year', 'benefit', 'position', 'work atmosphere', 'skill', 'description', 'job requirement', 'responsibility', 'requisition', 'personality', 'professional requirement', 'post', 'salary', 'you', 'other instruction', 'work location', 'job description', 'recommend', 'daily work', 'overview', 'today', 'job', 'skill requirement', 'welfare benefit', 'category', 'job benefit', 'job qualification', 'job responsibility', 'company introduction',\"overview\"] + ['Position', 'Responsibilities', 'Description', 'Benefits', 'Job Requirements', 'Job Responsibilities', 'Job Requirements', 'Job Description', 'Work Location', 'Recommended Skills', 'Professional Requirements', 'Skill Requirements', 'Education Requirements', 'Other Instructions', 'Company Introduction', 'Work Address', 'Work Hours', 'Personality', 'Skills', 'Experience', 'Company Introduction', 'Job Responsibilities', 'Job Requirements', 'Job Benefits', 'Welfare Benefits', 'Qualification', 'Work Atmosphere', 'Salary Benefits', 'Qualification', 'Daily Work'] + [\"At least a\",\"Requisition ID\",\"Today\",\"Summary\",\"g\",\"JOB SUMMARY\",\"Job Description : JOB SUMMARY\",\"tomorrow\", \"years\", \"include\",\"including\"]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T13:25:26.525097900Z",
     "start_time": "2023-11-27T13:25:26.459098Z"
    }
   },
   "id": "a2d5f0a2ca0c9105"
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前网站: boss直聘\n",
      "当前网站: CareerBuilder\n",
      "当前网站: CIA\n",
      "当前网站: DNI\n",
      "当前网站: linkedin\n",
      "当前网站: Simplyhired\n",
      "当前网站: 智联招聘\n",
      "英文数据:\t 1669 条数据\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [02:01<00:00, 12.11s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主题1 work, intelligence, experience, datum, business, information, team, analysis, provide, analyst, data, support, ability, employee, require, requirement, report, opportunity, security, knowledge, management, america, service, customer, process, degree, research, tool, system, employment, perform, develop, product, program, technology, time, development, applicant, project, technical, disability, source, status, analytic, federal, base, level, national, client, design\n",
      "主题2 experience, intelligence, support, analysis, work, information, business, datum, require, america, team, security, provide, system, mission, ability, analyst, development, customer, degree, opportunity, solution, service, management, develop, knowledge, data, level, report, employment, requirement, national, threat, employee, program, technical, government, process, source, research, tool, operation, technology, company, analytic, environment, product, time, status, health\n",
      "主题3 experience, business, security, intelligence, work, support, team, datum, analysis, information, data, degree, system, process, opportunity, customer, status, company, provide, analyst, time, america, national, require, develop, ability, technology, requirement, program, development, knowledge, base, operation, range, solution, employee, mission, client, report, technical, project, disability, service, level, analytic, environment, build, protect, health, management\n",
      "主题4 experience, intelligence, business, work, datum, support, team, employee, information, security, analysis, provide, analyst, management, status, require, system, opportunity, ability, develop, data, degree, customer, america, report, knowledge, base, process, product, company, level, source, relate, tool, development, analytical, disability, service, environment, community, program, bachelor, time, solution, application, strong, candidate, health, apply, requirement\n",
      "英文主题建模结果已保存至 ../Data/LDA主题建模/英文主题建模结果.html\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob(\"../Data/数据清洗/*\")\n",
    "stop_words = open(\"../Data/stop_words.txt\", encoding=\"utf-8\").read().split(\"\\n\")\n",
    "save_path = \"../Data/LDA主题建模/\"\n",
    "if not os.path.exists(save_path):\n",
    "    os.mkdir(save_path)\n",
    "    \n",
    "En_data = list()\n",
    "for file in files:\n",
    "    file_name = file.replace('\\\\', '/').split('/')[-1].split('.')[0]\n",
    "    print(f\"当前网站: {file_name}\")\n",
    "    df = pd.read_csv(file, index_col=0)\n",
    "    # 删除重复行\n",
    "    df = df.drop_duplicates(keep='last')\n",
    "    # 去掉缺失值\n",
    "    df = df.dropna(subset=['职位', '任职要求'])\n",
    "    if re.search('[\\u4e00-\\u9fa5]', file_name):  # 中文网站\n",
    "        pass\n",
    "    else:  # 英文网站\n",
    "        En_data += df[\"任职要求\"].tolist()\n",
    "print(\"英文数据:\\t\",len(En_data), \"条数据\")\n",
    "\n",
    "EnLDA().run(En_data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T13:29:50.018865800Z",
     "start_time": "2023-11-27T13:25:26.472097700Z"
    }
   },
   "id": "d9f2e740-75e6-44d0-ada3-d4dffa3bb655"
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "主题1 work, experience, intelligence, datum, business, analysis, support, team, information, provide, data, require, ability, analyst, opportunity, knowledge, report, customer, employee, management, security, requirement, program, degree, america, service, status, system, analytic, development, level, project, employment, develop, base, process, tool, time, national, disability, research, applicant, range, federal, technical, source, solution, perform, protect, company\n",
      "主题2 intelligence, experience, work, security, business, support, analysis, information, provide, degree, team, product, customer, require, system, datum, analyst, america, opportunity, employee, company, process, ability, source, time, research, national, develop, bachelor, requirement, level, management, technology, service, report, environment, individual, network, threat, development, analyze, status, solution, perform, data, operation, base, target, mission, federal\n",
      "主题3 experience, business, intelligence, support, work, analysis, security, information, datum, team, analyst, america, provide, system, employee, require, mission, ability, develop, data, management, opportunity, process, development, technology, solution, degree, environment, tool, requirement, knowledge, service, company, status, operation, technical, report, program, employment, client, customer, level, communication, application, source, ensure, time, health, perform, government\n",
      "英文主题建模结果已保存至 ../Data/LDA主题建模/英文主题建模结果.html\n"
     ]
    }
   ],
   "source": [
    "EnLDA().run2(En_data,3)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-27T14:05:11.087399Z",
     "start_time": "2023-11-27T14:02:14.518114500Z"
    }
   },
   "id": "84a684c7d1eeab56"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
